Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina. Detecting DR at an advanced stage often results in irreversible blindness. The traditional process of diagnosing DR through retina fundus images by ophthalmologists is not only time-intensive but also expensive. While classical transfer learning models have been widely adopted for computer-aided detection of DR, their high maintenance costs can hinder their detection efficiency. In contrast, Quantum Transfer Learning offers a more effective solution to this challenge. This approach is notably advantageous because it operates on heuristic principles, making it highly optimized for the task. Our proposed methodology leverages this hybrid quantum transfer learning technique to detect DR. To construct our model, we utilize the APTOS 2019 Blindness Detection dataset, available on Kaggle. We employ the ResNet-18, ResNet34, ResNet50, ResNet101, ResNet152 and Inception V3, pre-trained classical neural networks, for the initial feature extraction. For the classification stage, we use a Variational Quantum Classifier. Our hybrid quantum model has shown remarkable results, achieving an accuracy of 97% for ResNet-18. This demonstrates that quantum computing, when integrated with quantum machine learning, can perform tasks with a level of power and efficiency unattainable by classical computers alone. By harnessing these advanced technologies, we can significantly improve the detection and diagnosis of Diabetic Retinopathy, potentially saving many from the risk of blindness. Keywords: Diabetic Retinopathy, Quantum Transfer Learning, Deep Learning

翻译：糖尿病视网膜病变（DR）是糖尿病患者常见的并发症，视网膜上形成的病变可导致视力损伤。晚期DR的检测往往导致不可逆的失明。传统上，眼科医生通过视网膜眼底图像诊断DR的过程不仅耗时，而且费用高昂。尽管经典迁移学习模型已广泛用于DR的计算机辅助检测，但其高维护成本可能降低检测效率。相比之下，量子迁移学习为该挑战提供了更有效的解决方案。该方法因基于启发式原理运行而对任务高度优化，具有显著优势。我们提出的方法利用这种混合量子迁移学习技术检测DR。为构建模型，我们使用Kaggle平台上的APTOS 2019失明检测数据集。在初始特征提取阶段，采用预训练的经典神经网络ResNet-18、ResNet34、ResNet50、ResNet101、ResNet152及Inception V3；分类阶段则使用变分量子分类器。我们的混合量子模型取得了显著成果，其中ResNet-18的准确率达到97%。这表明，量子计算与量子机器学习结合时，能够以经典计算机无法企及的强大能力和效率执行任务。通过利用这些先进技术，我们可显著改善糖尿病视网膜病变的检测与诊断，有望使许多人免于失明风险。关键词：糖尿病视网膜病变，量子迁移学习，深度学习